Asset Prepayment Assumptions
RECORD OF SOCIETY
1995 VOL. 21 NO. 2
ASSET
Moderator:
Panelists:
Recorder:
PREPAYMENT
OF ACTUARIES
ASSUMPTIONS
STEPHEN D. REDDY
STEVE W. ABRAHAMS*
RANDALL L. BOUSHEK
CATHERINE E. EHRLICH
STEPHEN D. REDDY
This session will address asset prepayments from several perspectives. Presentations will
cover emerging prepayment experience in the mortgage-backed security arena, special
modeling considerations when addressing prepayments, and the initial experience and
impact of the NAIC flow uncertainty index (FLUX).
MR. STEPHEN D. REDDY: Asset prepayment assumptions and asset prepayments
themselves are a very important part of modeling assets and projecting future cash flows.
It's important to understand both what drives prepayments and also the total effect of
prepayments on an organization. We will cover asset prepayments from a couple different
angles. One will be a discussion of the NAIC FLUX model, which is a regulatory screening tool for measuring cash flow uncertainty and variability. Second will be a discussion of
what drives prepayments, particularly mortgage collateral, and modeling considerations
relating to those prepayment assumptions.
Our first speaker will be Randy Boushek, a vice president and portfolio manager at
Lutheran Brotherhood, which is a fraternal benefit society headquartered in Minneapolis,
with approximately $15 billion under management. His responsibilities include overseeing
all trading, research and portfolio management within the life company bond portfolio,
serving as Investment Division liaison on asset/liability management issues, directing
quantitative investment research, and overseeing derivative activities across all fixed
income portfolios. He's a member of the NAIC's technical resource groups on collateralized mortgage obligation (CMO) accounting and CMO cash flow volatility that developed
the FLUX model. He's a frequent speaker at both actuarial and investment conferences.
Second, we have Steve Abrahams, who's a vice president at Morgan Stanley & Company.
Steve is a member of the mortgage research group in the fixed income division and spends
a great deal of time studying mortgage prepayments in the residential sector. I think he has
some interesting things to say about what's been happening in prepayments the last couple
years and what Morgan Stanley is doing in its modeling efforts with respect to
prepayments.
Third, we have Catherine Ehrlich, who's a senior vice president at Capital Management
Sciences (CMS), which provides fixed income software systems and consulting services to
the investment management community. As a manager in the New York office, Catherine
is responsible for marketing and client support on the East Coast. Prior to joining CMS,
she was an assistant vice president with Metropolitan Life Insurance Company, where she
*Mr.Abrahams,nota memberof thesponsoringorganizations,isVicePresidentof MorganStanley,& Co.
inNew York,NY.
261
RECORD, VOLUME 21
had various assignments including cash-flow testing, pricing, and customer support for
institutional pension products. She earned her bachelor of arts degree at Colgate University
and is a Fellow of the Society of Actuaries and a Chartered Financial Analyst.
I'm Steve Reddy, also with Morgan Stanley. I work in the portfolio strategies group in the
Fixed Income Division in New York, doing asset/liability consulting, primarily for life
companies. I've had the pleasure of working with Steve. We're part of the same group, but
he's much more of an expert in the mortgage prepayment arena.
Randy will lead offand talk about the FLUX system, and then we'll get into some of the
other issues involving mortgage prepayments and modeling considerations relating to that.
MR. RANDALL L. BOUSHEK: My assignment on the panel is to discuss the NAIC's
FLUX model and to tie that discussion to the general topic of prepayment assumptions. As
Steve mentioned, I am a member of the technical resource group that developed the FLUX
model under the auspices and at the direction of the NAIC's invested asset working group
(IAWG).
My outline for this presentation consists of six points. First, I would like to discuss in
general what the FLUX model is and, perhaps, more importantly, what it is not. Second, I
want to provide a brief history of the model's development and discuss some of the
considerations that led it to take its current form. Third, and most germane to this session, I
want to focus on the scenario specification and prepayment assumptions that are necessarily
a part of the FLUX model. Fourth, and with a promise to avoid all formulas, I want to
discuss the functional mechanics of the model. Fiffia, I'd like to review with you a distribution of actual FLUX scores for 1994. Finally, I'd like to comment briefly on one or two
open issues.
Just what is FLUX? The FLUX model is a regulatory screening tool. It was developed
specifically for insurance regulators for the sole purpose of enabling them to narrow the
multitude of cash-flow testing reports that they receive to a more manageable few that may
require closer scrutiny because of potential CMO cash-flow volatility. The FLUX model is
specifically not a rating mechanism, nor a tool for establishing reserving requirements or
pass/fail tests. It is also not a portfolio management tool. To clear up a bit of confusion, it
should also be emphasized that FLUX is a calculation model, not a prepayment model. It
does use and set the specification for a set of prepayment assumptions provided by the
Public Securities Association (PSA), but it is neither a prepayment model nor a valuation
model of any kind.
The technical resource group which developed the model was comprised of representatives
from Wall Street, the insurance industry, and investment software vendors who provided
much of the horsepower for testing various designs and aspects of the model. Our general
charge from the IAWG was to develop a methodology for assessing the relative cash flow
volatility of individual CMO tranches, with three or four specific constraints. Two key
words here are relative and individual. The FLUX model provides a relative measure of
volatility exposure--there
is no absolute interpretation to any FLUX score. Further, the
FLUX model only attempts to quantify the potential for cash flow volatility without
assessing whether that volatility would be good or bad for the holder. More contentiously,
262
ASSET PREPAYMENT ASSUMPTIONS
the model is specifically designed to evaluate bonds on a trancbe-by-tranche (as opposed to
aggregate-portfolio) basis, at the direction of the IAWG.
There were three other specific guidelines provided by the IAWG. First, the model was to
have an "open architecture," meaning that all formulas were to be publicly available and
reproducible by any interested party, and all assumptions were to be under control of the
regulators. No "black box" approaches were acceptable. Second, the model was to produce
a single score for each CMO wanche across all companies, that is, a score that is independent of book value. Certainly, ifI own a bond at a price of 80, and you own the same bond
at a price of 120, we have very different statutory risk exposures. Nonetheless, given its
specific objective, the model was expressly developed to be independent of holding price.
Finally, the model had to be as simple as possible.
As to the development process itself, we initially considered three different models submitted by members of the technical resource group. The FLUX model in its original form was
developed and submitted by Andrew Davidson. It underwent several refinements over the
course of more than a year before it was submitted in final form to the IAWG. Beyond the
design of the model itself, several other issues had to be addressed: How many scenarios,
and which ones? How do we combine results from various scenarios? What prepayment
assumptions do we use? How do we accommodate floating rate instruments? I'll address
scenarios and prepayment assumptions in a moment. The question of combination is
actually quite interesting. The FLUX model produces a single numerical value, or score,
for each CMO tranche for each scenario. Given a vector of scores corresponding to a set of
scenarios for a given tranche, one might make an argument (and more than one did) for
either the maximum or mean as the best representative score for the bond. In the end, after
considerable deliberation, we settled on root mean square as the most acceptable
compromise.
The specification of scenarios and prepayment assumptions is designed to be a dynamic
process, under the control of insurance regulators. Striking a balance between simplicity
and a representative range of outcomes is particularly difficult here. Based in part on
spanning set research by Dr. Thomas Ho, an initial set of five nonlevel interest rate
scenarios, plus a base case level rate scenario, was established for the test year 1993. These
five scenarios included two increasing rate paths, two decreasing rate paths, and one
interest rate whipsaw. With the benefit of further research, a second whipsaw scenario was
added for 1994, and the oscillation period of the earlier whipsaw was compressed. The
addition, deletion, or alteration of scenarios in the future is entirely at the discretion of
regulators.
Once the scenarios have been established and a freeze date is set to determine the initial
level of interest rates, dealers are surveyed fortheir prepayment projections as of the freeze
date for a three-dimensional array (agency, program, and coupon) of pass-through collateral
for the specified scenarios. All such projections are provided as a vector of monthly
prepayment assumptions for each scenario for each collateral cell in the matrix. Once this
information has been received, a median speed is calculated for each month in each vector.
The resulting matrix of vectors of monthly median speeds then becomes the "official" set of
prepayment assumptions for the FLUX model. At any point in time, any broker, vendor, or
insurer can calculate "current" FLUX scores using their own proprietary prepayment
263
RECORD, VOLUME 21
assumptions; however, the NAIC's "annual" FLUX scores are based solely on the official
set of prepayment assumptions, which are publicly available to any interested party.
While the FLUX model utilizes an "official" set of prepayment assumptions, there is
technically no "official" FLUX score for any bond. Given the prepayment vectors determined above, and the discount rate and volatility assumptions which rll discuss in a
moment, any broker, vendor, or insurer with an accurate CMO structuring model should be
able to produce the same cash flows and FLUX scores for any CMO tranche. Annual
scores are submitted to the NAIC electronically by broker dealers and distributed to
regulators via the state data network. For simplicity, the bulk of the scores are submitted
by Merrill Lynch via its Passport system, with scores for bonds not modeled in Passport
submitted by other brokers. Life companies are not responsible for either calculating or
reporting FLUX scores.
Regulators can electronically match a company's Schedule D CMO holdings to the master
list of FLUX scores on the state data network for their own analysis. Since this is the
limited purpose that the FLUX model was designed for, FLUX scores are not published in
the insurer's financial statements.
Beyond prepayments, the FLUX model requires two other input assumptions: discount rate
and volatility. These assumptions are also set annually by the NAIC. The discount rate is,
as one might expect, an interest rate used to discount cash flows in the calculation model.
However, the volatility assumption is a specific variable in a few of the FLUX model
calculations and not the more typical controlling input into a stochastic interest rate
generator or options model. The technical resource group did develop a formulaic
approach for determining these assumptions, but as promised earlier I will not get into any
specifics here. Suffice it to say that on the basis of this approach the discount rate for 1993
and 1994 was set at 6% and 7.50%, respectively, while the volatility rate was set at 1.75%
for 1993 and 2% for 1994.
The FLUX score for any given bond for any given scenario is the sum of two components--a present value measure and a timing measure. The present value measure reflects
the magnitude of negative percentage change in present value in each scenario relative to
the base case. The timing measure represents the sum of period-by-period differences in
scaled cumulative present value of cash flows in each scenario relative to the base case.
The present value measure is designed to capture the risk of adverse prepayments on the
valuation of bonds priced at a significant premium or discount to par, for example, interest
only (IOs) and principal only (POs). The timing measure is designed to capture the risk of
adverse prepayments on the reinvestment of cash flows, particularly for companion/support
and "jump" tranches. Two key words in these definitions are negative and absolute, and
they underscore a key point about the FLUX model. Namely, that it assesses only the
potential volatility of cash flows without regard to direction (shortening or extending) and
with no offset for beneficial impact. This also helps explain why the sum of the parts, that
is, the weighted average FLUX score for each tranche in a CMO or in the simplest case for
an IO and a PO, almost always exceeds the whole, or the FLUX score, for the underlying
collateral.
264
ASSET PREPAYMENT ASSUMPTIONS
Table 1 shows the distribution of annual FLUX scores for 1994 for the CMO universe as
defined by Bloomberg, excluding those bonds for which no FLUX score was submitted.
The information presented here includes FLUX scores for approximately 33,000 tranches.
The first line shows the distribution of FLUX scores by number of tranches, while the
second line is the distribution by dollar value. On a dollar-weighted basis, over 50% of the
tranches in the universe had a FLUX score below three at year-end 1994, while almost 75%
were below five. In general, 1994 scores are lower than 1993 scores, owing to changes in
the starting level of interest rates, projected prepayment assumptions, and the discount rate
and volatility assumptions. For the sake of comparison, I have also included the FLUX
score for a current coupon Government National Mortgage Association (GNMA)
pass-through, which is actually higher than the score for more than half of the dollarweighted CMO universe. It is interesting to note that the same GNMA pass-through had a
FLUX score in excess of six for 1993.
TABLE 1
DISTRIBUTIONOF 1994 FLUX SCORES
0-1
1-3
3-5
5-10
10-20
> 20
Average
CMO Universe
37%
22%
12%
11%
7%
11%
6.6
CMO Universe
(S-weighted)
18%
35%
21%
14%
6%
6%
5.3
?
?
?
?
?
?
?
Life Co Holdings
(S-weighted)
FLUX Scorefor 30-year GNMA 8% Pass-Through:3.6
Source:Bloomberg
LP5/04_95(universeincludes33,000 tranches)
It is important to remember that "high" FLUX scores are not necessarily bad. High scores
indicate the potential for cash-flow volatility. This is not the same as indicating a high
level of risk. A concentration of high scores may conceal offsetting risks within the
portfolio, or ignore offsetting risks or liability exposures outside of the portfolio. High
FLUX scores are simply a cue to regulators that a closer look at actuarial cash-flow testing
results may be warranted.
I'd like to comment on one or two other items before closing my remarks. First, I mentioned earlier that "current" FLUX scores can be computed at any time by any broker,
vendor or insurer. Beyond the obvious difference in prepayment assumptions, these scores
will vary from "annual" FLUX scores for two other reasons (even if the annual prepayment
vectors are used). For one thing, the starting point for interest rate scenarios will in all
likelihood be different, and for another, some principal may have already paid down since
the freeze date for the annual scores. As a caveat, current scores are not necessarily
indicative at any point in time of subsequent annual scores.
Second, I also mentioned earlier that FLUX was specifically designed as a bond-by-bond
tool. While it is theoretically possible to calculate a portfolio FLUX score by aggregating
all projected cash flows before calculating the timing and present value measures, the
usefulness of such a number is debatable. Beyond the desire of regulators to be able to
independently assess the potential cash-flow volatility of individual investments, the
265
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